This is the first book to focus on a hybrid of compressive and urban sensing. It explains how reliable imaging, tracking, and localization of indoor targets can be achieved using compressed observations that amount to a tiny percentage of the entire data volume. Capturing the latest and most important advances in the field, this state-of-the-art text includes successful applications of compressive sensing for target detection and revealing building interiors, sparse reconstruction techniques for urban environments, and supporting examples using real data and computational electromagnetic modeling.…mehr
This is the first book to focus on a hybrid of compressive and urban sensing. It explains how reliable imaging, tracking, and localization of indoor targets can be achieved using compressed observations that amount to a tiny percentage of the entire data volume. Capturing the latest and most important advances in the field, this state-of-the-art text includes successful applications of compressive sensing for target detection and revealing building interiors, sparse reconstruction techniques for urban environments, and supporting examples using real data and computational electromagnetic modeling.
Dr. Moeness G. Amin has been a faculty member of the Department of Electrical and Computer Engineering at Villanova University, Pennsylvania, USA for nearly 30 years. In 2002, he became the director of the Center for Advanced Communications, College of Engineering. Currently he is the chair of the Electrical Cluster of the Franklin Institute Committee on Science and the Arts, as well as an IEEE, SPIE, and IET fellow. The recipient of many prestigious awards, he has conducted extensive research in radar signal processing, authored over 650 journal and conference papers, and served as an editor for numerous publications.
Inhaltsangabe
Compressive Sensing Fundamentals. Overcomplete Dictionary Design for Sparse Reconstruction of Building Layout Mapping. Compressive Sensing for Radar Imaging of Underground Targets. Wall Clutter Mitigations for Compressive Imaging of Building Interiors. Compressive Sensing for Urban Multipath Exploitation. Compressive Sensing Kernel Design for Imaging of Urban Objects. Compressive Sensing for Multi-Polarization Through-Wall Radar Imaging. Sparseness-Aware Human Motion Indication. Time-Frequency Analysis of Micro-Doppler Signals based on Compressive Sensing. Urban Target Tracking using Sparse Representations. 3D Imaging of Vehicles from Sparse Apertures in Urban Environment. Compressive Sensing for MIMO Urban Radar. Compressive Sensing Meets Noise Radar.
Compressive Sensing Fundamentals. Overcomplete Dictionary Design for Sparse Reconstruction of Building Layout Mapping. Compressive Sensing for Radar Imaging of Underground Targets. Wall Clutter Mitigations for Compressive Imaging of Building Interiors. Compressive Sensing for Urban Multipath Exploitation. Compressive Sensing Kernel Design for Imaging of Urban Objects. Compressive Sensing for Multi-Polarization Through-Wall Radar Imaging. Sparseness-Aware Human Motion Indication. Time-Frequency Analysis of Micro-Doppler Signals based on Compressive Sensing. Urban Target Tracking using Sparse Representations. 3D Imaging of Vehicles from Sparse Apertures in Urban Environment. Compressive Sensing for MIMO Urban Radar. Compressive Sensing Meets Noise Radar.
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